Computer vision is the science of electronically perceiving and understanding images and includes a wide variety of practical applications. For example, computer vision can be used in navigation, visual surveillance, human-computer interaction, etc.
In computer vision, the quality of captured images is a critical factor in the ability of a computing device to process the captured images to detect, localize, recognize, and/or identify objects. Accordingly, many computer vision systems are often forced to account for worst-case image conditions by using high-cost equipment and/or high-cost methods to obtain high quality images. However, such equipment and methods may not be necessary for every imaging condition.
As an example, a computer vision system set up to perform automatic license plate recognition (ALPR) may require flash illumination to obtain images that can be processed during periods of low ambient illumination. However, utilizing flash illumination may lead to additional expenses as it may require lamp replacement after a certain number of flashes. Further, the same system may not require flash illumination during periods of high ambient illumination.
Therefore, computer vision techniques can be improved by methods and systems that optimize the use of high-cost computer vision equipment and methods.